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ALMEERAEDUCATIONALMEERAEDUCATION

Journal Jurnal Penalaran dan Riset MatematikaJurnal Penalaran dan Riset Matematika Abstract

Learning statistics remains a persistent challenge for many junior high school students due to limited engagement. Improving students understanding and motivation in statistics learning requires innovative approaches that promote active, meaningful participation. This study investigates the impact of a novel deep learning-based Jigsaw model on Grade VIII students engagement and statistics learning outcomes at SMPN 2 Mojosari. Integrating principles of mindful, meaningful, and joyful learning, the model emphasizes understanding, application, and reflection to foster deeper cognitive engagement. Using a quantitative one-shot case study design with 32 students, data were gathered through student activeness questionnaires and statistics achievement tests. Findings reveal that this approach significantly enhances student activeness, promoting collaboration and critical thinking during discussions, presentations, and reflections. Most students demonstrated medium to high engagement levels, while average learning outcomes surpassed the schools minimum mastery criteria. Given the urgent need to improve statistical literacy and student motivation in junior high schools, the deep learning-based Jigsaw model offers a promising, effective alternative strategy to traditional teaching methods. This research contributes valuable evidence supporting innovative, student-centered learning in mathematics education.

Conclusion

This research concludes that implementing the Jigsaw cooperative learning model integrated with deep learning approaches positively and significantly impacts student activeness and learning outcomes in statistics.Data indicates that the majority of students (84.4%) exhibited medium to high levels of activeness, accompanied by statistically significant improvements in learning outcomes.These findings support the effectiveness of combining cooperative learning and deep learning principles to optimize mathematics learning processes and foster a more comprehensive and meaningful learning experience.

Future Research

Berdasarkan hasil penelitian ini, terdapat beberapa saran penelitian lanjutan yang dapat dilakukan untuk memperluas pemahaman mengenai efektivitas model pembelajaran Jigsaw berbasis deep learning. Pertama, penelitian lebih lanjut perlu dilakukan dengan melibatkan sampel yang lebih besar dan beragam, mencakup berbagai tingkatan kelas dan karakteristik siswa, untuk menguji generalisasi temuan penelitian ini. Kedua, penelitian dapat difokuskan pada eksplorasi lebih mendalam mengenai mekanisme bagaimana prinsip-prinsip deep learning, seperti refleksi dan kolaborasi, berkontribusi pada peningkatan aktivitas dan hasil belajar siswa dalam konteks pembelajaran statistik. Ketiga, penelitian lanjutan dapat menginvestigasi integrasi teknologi digital, seperti platform pembelajaran online atau aplikasi gamifikasi, ke dalam model Jigsaw berbasis deep learning untuk meningkatkan keterlibatan siswa dan memfasilitasi pembelajaran yang lebih personal dan adaptif. Penelitian-penelitian ini diharapkan dapat memberikan kontribusi signifikan dalam pengembangan model pembelajaran matematika yang inovatif dan efektif, serta meningkatkan kualitas pendidikan secara keseluruhan.

References

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File size	264.59 KB
Pages	10
DMCA	Report